diff --git a/.nojekyll b/.nojekyll
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diff --git a/reference/TimeSeriesCV.html b/reference/TimeSeriesCV.html
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@@ -672,9 +672,9 @@ <h2 class="anchored" data-anchor-id="examples">Examples:</h2>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>tscv.plot(y)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -720,7 +720,7 @@ <h2 class="anchored" data-anchor-id="methods">Methods</h2>
 </tr>
 <tr class="odd">
 <td><a href="#pytimetk.TimeSeriesCV.split">split</a></td>
-<td>Returns a generator of split arrays with an optional <code>split_limit</code>.</td>
+<td>Returns a generator of split arrays.</td>
 </tr>
 </tbody>
 </table>
@@ -831,11 +831,64 @@ <h4 class="anchored" data-anchor-id="parameters-1">Parameters</h4>
 <section id="pytimetk.TimeSeriesCV.split" class="level3">
 <h3 class="anchored" data-anchor-id="pytimetk.TimeSeriesCV.split">split</h3>
 <p><code>TimeSeriesCV.split(*arrays, time_series=None, start_dt=None, end_dt=None, return_splitstate=False)</code></p>
-<p>Returns a generator of split arrays with an optional <code>split_limit</code>.</p>
-<p>Arguments: *arrays: The arrays to split. Must have the same length as <code>time_series</code>. time_series: The time series used to create boolean masks for splits. If not provided, the method will try to use the index of the first array (if it is a DataFrame or Series) as the time series. start_dt: The start of the time period. If provided, it is used in place of <code>time_series.min()</code>. end_dt: The end of the time period. If provided, it is used in place of <code>time_series.max()</code>. return_splitstate: Whether to return the <code>SplitState</code> instance for each split.</p>
-<p>Yields: A generator of tuples of arrays containing the training and forecast data. If <code>split_limit</code> is set, yields only up to <code>split_limit</code> splits.</p>
+<p>Returns a generator of split arrays.</p>
+<section id="parameters-2" class="level4">
+<h4 class="anchored" data-anchor-id="parameters-2">Parameters</h4>
+<table class="table">
+<colgroup>
+<col style="width: 8%">
+<col style="width: 11%">
+<col style="width: 75%">
+<col style="width: 4%">
+</colgroup>
+<thead>
+<tr class="header">
+<th>Name</th>
+<th>Type</th>
+<th>Description</th>
+<th>Default</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td><code>*arrays</code></td>
+<td>TL</td>
+<td>The arrays to split. Must have the same length as <code>time_series</code>.</td>
+<td><code>()</code></td>
+</tr>
+<tr class="even">
+<td><code>time_series</code></td>
+<td>SeriesLike[DateTimeLike]</td>
+<td>The time series used to create boolean masks for splits. If not provided, the method will try to use the index of the first array (if it is a DataFrame or Series) as the time series.</td>
+<td><code>None</code></td>
+</tr>
+<tr class="odd">
+<td><code>start_dt</code></td>
+<td>NullableDatetime</td>
+<td>The start of the time period. If provided, it is used in place of <code>time_series.min()</code>.</td>
+<td><code>None</code></td>
+</tr>
+<tr class="even">
+<td><code>end_dt</code></td>
+<td>NullableDatetime</td>
+<td>The end of the time period. If provided, it is used in place of <code>time_series.max()</code>.</td>
+<td><code>None</code></td>
+</tr>
+<tr class="odd">
+<td><code>return_splitstate</code></td>
+<td>bool</td>
+<td>Whether to return the <code>SplitState</code> instance for each split.</td>
+<td><code>False</code></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="returns" class="level4">
+<h4 class="anchored" data-anchor-id="returns">Returns:</h4>
+<p>A generator of tuples of arrays containing the training and forecast data. If <code>split_limit</code> is set, yields only up to <code>split_limit</code> splits.</p>
 
 
+</section>
 </section>
 </section>
 
diff --git a/search.json b/search.json
index 58045fb6..d0cd8ca9 100644
--- a/search.json
+++ b/search.json
@@ -2426,7 +2426,7 @@
     "href": "reference/TimeSeriesCV.html#methods",
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-    "text": "Methods\n\n\n\nName\nDescription\n\n\n\n\nglimpse\nPrints summary information about the splits, focusing on the first two arrays.\n\n\nplot\nPlots the cross-validation folds on a single plot with folds on the y-axis and dates on the x-axis using filled Scatter traces.\n\n\nsplit\nReturns a generator of split arrays with an optional split_limit.\n\n\n\n\nglimpse\nTimeSeriesCV.glimpse(*arrays, time_series=None)\nPrints summary information about the splits, focusing on the first two arrays.\nArguments: *arrays: The arrays to split. Only the first one will be used for summary information. time_series: The time series used for splitting. If not provided, the index of the first array is used. Default is None.\n\n\nplot\nTimeSeriesCV.plot(y, time_series=None, color_palette=None, bar_height=0.3, title='Time Series Cross-Validation Plot', x_lab='', y_lab='Fold', x_axis_date_labels=None, base_size=11, width=None, height=None, engine='plotly')\nPlots the cross-validation folds on a single plot with folds on the y-axis and dates on the x-axis using filled Scatter traces.\n\nParameters\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ny\npd.Series\nThe target time series as a pandas Series.\nrequired\n\n\ntime_series\npd.Series\nThe time series used for splitting. If not provided, the index of y is used. Default is None.\nNone\n\n\ncolor_palette\nOptional[Union[dict, list, str]]\nThe color palette to use for the train and forecast. If not provided, the default colors are used.\nNone\n\n\nbar_height\nfloat\nThe height of each bar in the plot. Default is 0.3.\n0.3\n\n\ntitle\nstr\nThe title of the plot. Default is “Time Series Cross-Validation Plot”.\n'Time Series Cross-Validation Plot'\n\n\nx_lab\nstr\nThe label for the x-axis. Default is ““.\n''\n\n\ny_lab\nstr\nThe label for the y-axis. Default is “Fold”.\n'Fold'\n\n\nx_axis_date_labels\nstr\nThe format of the date labels on the x-axis. Default is None.\nNone\n\n\nbase_size\nfloat\nThe base font size for the plot. Default is 11.\n11\n\n\nwidth\nOptional[int]\nThe width of the plot in pixels. Default is None.\nNone\n\n\nheight\nOptional[int]\nThe height of the plot in pixels. Default is None.\nNone\n\n\nengine\nstr\nThe plotting engine to use. Default is “plotly”.\n'plotly'\n\n\n\n\n\n\nsplit\nTimeSeriesCV.split(*arrays, time_series=None, start_dt=None, end_dt=None, return_splitstate=False)\nReturns a generator of split arrays with an optional split_limit.\nArguments: *arrays: The arrays to split. Must have the same length as time_series. time_series: The time series used to create boolean masks for splits. If not provided, the method will try to use the index of the first array (if it is a DataFrame or Series) as the time series. start_dt: The start of the time period. If provided, it is used in place of time_series.min(). end_dt: The end of the time period. If provided, it is used in place of time_series.max(). return_splitstate: Whether to return the SplitState instance for each split.\nYields: A generator of tuples of arrays containing the training and forecast data. If split_limit is set, yields only up to split_limit splits."
+    "text": "Methods\n\n\n\nName\nDescription\n\n\n\n\nglimpse\nPrints summary information about the splits, focusing on the first two arrays.\n\n\nplot\nPlots the cross-validation folds on a single plot with folds on the y-axis and dates on the x-axis using filled Scatter traces.\n\n\nsplit\nReturns a generator of split arrays.\n\n\n\n\nglimpse\nTimeSeriesCV.glimpse(*arrays, time_series=None)\nPrints summary information about the splits, focusing on the first two arrays.\nArguments: *arrays: The arrays to split. Only the first one will be used for summary information. time_series: The time series used for splitting. If not provided, the index of the first array is used. Default is None.\n\n\nplot\nTimeSeriesCV.plot(y, time_series=None, color_palette=None, bar_height=0.3, title='Time Series Cross-Validation Plot', x_lab='', y_lab='Fold', x_axis_date_labels=None, base_size=11, width=None, height=None, engine='plotly')\nPlots the cross-validation folds on a single plot with folds on the y-axis and dates on the x-axis using filled Scatter traces.\n\nParameters\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ny\npd.Series\nThe target time series as a pandas Series.\nrequired\n\n\ntime_series\npd.Series\nThe time series used for splitting. If not provided, the index of y is used. Default is None.\nNone\n\n\ncolor_palette\nOptional[Union[dict, list, str]]\nThe color palette to use for the train and forecast. If not provided, the default colors are used.\nNone\n\n\nbar_height\nfloat\nThe height of each bar in the plot. Default is 0.3.\n0.3\n\n\ntitle\nstr\nThe title of the plot. Default is “Time Series Cross-Validation Plot”.\n'Time Series Cross-Validation Plot'\n\n\nx_lab\nstr\nThe label for the x-axis. Default is ““.\n''\n\n\ny_lab\nstr\nThe label for the y-axis. Default is “Fold”.\n'Fold'\n\n\nx_axis_date_labels\nstr\nThe format of the date labels on the x-axis. Default is None.\nNone\n\n\nbase_size\nfloat\nThe base font size for the plot. Default is 11.\n11\n\n\nwidth\nOptional[int]\nThe width of the plot in pixels. Default is None.\nNone\n\n\nheight\nOptional[int]\nThe height of the plot in pixels. Default is None.\nNone\n\n\nengine\nstr\nThe plotting engine to use. Default is “plotly”.\n'plotly'\n\n\n\n\n\n\nsplit\nTimeSeriesCV.split(*arrays, time_series=None, start_dt=None, end_dt=None, return_splitstate=False)\nReturns a generator of split arrays.\n\nParameters\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\n*arrays\nTL\nThe arrays to split. Must have the same length as time_series.\n()\n\n\ntime_series\nSeriesLike[DateTimeLike]\nThe time series used to create boolean masks for splits. If not provided, the method will try to use the index of the first array (if it is a DataFrame or Series) as the time series.\nNone\n\n\nstart_dt\nNullableDatetime\nThe start of the time period. If provided, it is used in place of time_series.min().\nNone\n\n\nend_dt\nNullableDatetime\nThe end of the time period. If provided, it is used in place of time_series.max().\nNone\n\n\nreturn_splitstate\nbool\nWhether to return the SplitState instance for each split.\nFalse\n\n\n\n\n\nReturns:\nA generator of tuples of arrays containing the training and forecast data. If split_limit is set, yields only up to split_limit splits."
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