DOC: update the pandas.Series.str.split docstring
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```
################################################################################
##################### Docstring (pandas.Series.str.split)  #####################
################################################################################

Split strings around given separator/delimiter.

Split each str in the caller's values by given
pattern, propagating NaN values. Equivalent to :meth:`str.split`.

Parameters
----------
pat : string, default None
    String or regular expression to split on.
    If `None`, split on whitespace.
n : int, default -1 (all)
    Vary dimensionality of output.

    * `None`, 0 and -1 will be interpreted as return all splits
expand : bool, default False
    Expand the split strings into separate columns.

    * If `True`, return DataFrame/MultiIndex expanding dimensionality.
    * If `False`, return Series/Index.

Returns
-------
Type matches caller unless `expand=True` (return type is `DataFrame`)
split : Series/Index or DataFrame/MultiIndex of objects

Notes
-----
If `expand` parameter is `True` and:
  - If n >= default splits, makes all splits
  - If n < default splits, makes first n splits only
  - Appends `None` for padding.

Examples
--------
>>> s = pd.Series(["this is good text", "but this is even better"])

By default, split will return an object of the same size
having lists containing the split elements

>>> s.str.split()
0           [this, is, good, text]
1    [but, this, is, even, better]
dtype: object
>>> s.str.split("random")
0          [this is good text]
1    [but this is even better]
dtype: object

When using `expand=True`, the split elements will
expand out into separate columns.

>>> s.str.split(expand=True)
      0     1     2     3       4
0  this    is  good  text    None
1   but  this    is  even  better
>>> s.str.split(" is ", expand=True)
          0            1
0      this    good text
1  but this  even better

Parameter `n` can be used to limit the number of columns in
expansion of output.

>>> s.str.split("is", n=1, expand=True)
        0                1
0      th     is good text
1  but th   is even better

If NaN is present, it is propagated throughout the columns
during the split.

>>> s = pd.Series(["this is good text", "but this is even better", np.nan])
>>> s.str.split(n=3, expand=True)
      0     1     2            3
0  this    is  good         text
1   but  this    is  even better
2   NaN   NaN   NaN          NaN

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameter "n" description should finish with "."
	See Also section not found
```