diff --git a/doc/source/api/series.rst b/doc/source/api/series.rst
index 7d5e6037b012a..8e4c378b9fefe 100644
--- a/doc/source/api/series.rst
+++ b/doc/source/api/series.rst
@@ -26,6 +26,7 @@ Attributes
 .. autosummary::
    :toctree: generated/
 
+   Series.array
    Series.values
    Series.dtype
    Series.ftype
@@ -58,10 +59,12 @@ Conversion
    Series.convert_objects
    Series.copy
    Series.bool
+   Series.to_numpy
    Series.to_period
    Series.to_timestamp
    Series.to_list
    Series.get_values
+   Series.__array__
 
 Indexing, iteration
 -------------------
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 3128e67f86873..f416e638b9176 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -662,12 +662,53 @@ def view(self, dtype=None):
     # ----------------------------------------------------------------------
     # NDArray Compat
 
-    def __array__(self, result=None):
+    def __array__(self, dtype=None):
         """
-        The array interface, return my values.
+        Return the values as a NumPy array.
+
+        Users should not call this directly. Rather, it is invoked by
+        :func:`numpy.array` and :func:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to use for the resulting NumPy array. By default,
+            the dtype is inferred from the data.
+
+        Returns
+        -------
+        numpy.ndarray
+            The values in the series converted to a :class:`numpy.ndarary`
+            with the specified `dtype`.
+
+        See Also
+        --------
+        Series.array : Zero-copy view to the array backing the Series.
+        Series.to_numpy : Series method for similar behavior.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+        >>> np.asarray(ser)
+        array([1, 2, 3])
+
+        For timezone-aware data, the timezones may be retained with
+        ``dtype='object'``
+
+        >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
+        >>> np.asarray(tzser, dtype="object")
+        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
+               Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
+              dtype=object)
+
+        Or the values may be localized to UTC and the tzinfo discared with
+        ``dtype='datetime64[ns]'``
+
+        >>> np.asarray(tzser, dtype="datetime64[ns]")  # doctest: +ELLIPSIS
+        array(['1999-12-31T23:00:00.000000000', ...],
+              dtype='datetime64[ns]')
         """
-        # TODO: change the keyword name from result to dtype?
-        if (result is None and isinstance(self.array, ABCDatetimeArray)
+        if (dtype is None and isinstance(self.array, ABCDatetimeArray)
                 and getattr(self.dtype, 'tz', None)):
             msg = (
                 "Converting timezone-aware DatetimeArray to timezone-naive "
@@ -678,8 +719,8 @@ def __array__(self, result=None):
                 "To keep the old behavior, pass 'dtype=\"datetime64[ns]\"'."
             )
             warnings.warn(msg, FutureWarning, stacklevel=3)
-            result = 'M8[ns]'
-        return np.asarray(self.array, result)
+            dtype = 'M8[ns]'
+        return np.asarray(self.array, dtype)
 
     def __array_wrap__(self, result, context=None):
         """