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diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 8e3e35a510c58..9d0b213e44671 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -3509,41 +3509,85 @@ def pipe(self, func: Callable, *args, **kwargs):
 
         .. code-block:: python
 
-            f(g(df.style.set_precision(3), arg1=a), arg2=b, arg3=c)
+            f(g(df.style.format(precision=3), arg1=a), arg2=b, arg3=c)
 
         users can write:
 
         .. code-block:: python
 
-            (df.style.set_precision(3)
+            (df.style.format(precision=3)
                .pipe(g, arg1=a)
                .pipe(f, arg2=b, arg3=c))
 
         In particular, this allows users to define functions that take a
         styler object, along with other parameters, and return the styler after
         making styling changes (such as calling :meth:`Styler.apply` or
-        :meth:`Styler.set_properties`).  Using ``.pipe``, these user-defined
-        style "transformations" can be interleaved with calls to the built-in
-        Styler interface.
+        :meth:`Styler.set_properties`).
 
         Examples
         --------
-        >>> def format_conversion(styler):
-        ...     return (styler.set_properties(**{'text-align': 'right'})
-        ...                   .format({'conversion': '{:.1%}'}))
-
-        The user-defined ``format_conversion`` function above can be called
-        within a sequence of other style modifications:
-
-        >>> df = pd.DataFrame({'trial': list(range(5)),
-        ...                    'conversion': [0.75, 0.85, np.nan, 0.7, 0.72]})
-        >>> (df.style
-        ...    .highlight_min(subset=['conversion'], color='yellow')
-        ...    .pipe(format_conversion)
-        ...    .set_caption("Results with minimum conversion highlighted."))
-        ...  # doctest: +SKIP
 
-        .. figure:: ../../_static/style/df_pipe.png
+        **Common Use**
+
+        A common usage pattern is to pre-define styling operations which
+        can be easily applied to a generic styler in a single ``pipe`` call.
+
+        >>> def some_highlights(styler, min_color="red", max_color="blue"):
+        ...      styler.highlight_min(color=min_color, axis=None)
+        ...      styler.highlight_max(color=max_color, axis=None)
+        ...      styler.highlight_null()
+        ...      return styler
+        >>> df = pd.DataFrame([[1, 2, 3, pd.NA], [pd.NA, 4, 5, 6]], dtype="Int64")
+        >>> df.style.pipe(some_highlights, min_color="green")  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/df_pipe_hl.png
+
+        Since the method returns a ``Styler`` object it can be chained with other
+        methods as if applying the underlying highlighters directly.
+
+        >>> df.style.format("{:.1f}")
+        ...         .pipe(some_highlights, min_color="green")
+        ...         .highlight_between(left=2, right=5)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/df_pipe_hl2.png
+
+        **Advanced Use**
+
+        Sometimes it may be necessary to pre-define styling functions, but in the case
+        where those functions rely on the styler, data or context. Since
+        ``Styler.use`` and ``Styler.export`` are designed to be non-data dependent,
+        they cannot be used for this purpose. Additionally the ``Styler.apply``
+        and ``Styler.format`` type methods are not context aware, so a solution
+        is to use ``pipe`` to dynamically wrap this functionality.
+
+        Suppose we want to code a generic styling function that highlights the final
+        level of a MultiIndex. The number of levels in the Index is dynamic so we
+        need the ``Styler`` context to define the level.
+
+        >>> def highlight_last_level(styler):
+        ...     return styler.apply_index(
+        ...         lambda v: "background-color: pink; color: yellow", axis="columns",
+        ...         level=styler.columns.nlevels-1
+        ...     )  # doctest: +SKIP
+        >>> df.columns = pd.MultiIndex.from_product([["A", "B"], ["X", "Y"]])
+        >>> df.style.pipe(highlight_last_level)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/df_pipe_applymap.png
+
+        Additionally suppose we want to highlight a column header if there is any
+        missing data in that column.
+        In this case we need the data object itself to determine the effect on the
+        column headers.
+
+        >>> def highlight_header_missing(styler, level):
+        ...     def dynamic_highlight(s):
+        ...         return np.where(
+        ...             styler.data.isna().any(), "background-color: red;", ""
+        ...         )
+        ...     return styler.apply_index(dynamic_highlight, axis=1, level=level)
+        >>> df.style.pipe(highlight_header_missing, level=1)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/df_pipe_applydata.png
         """
         return com.pipe(self, func, *args, **kwargs)