DOC: update the pandas.Series.dt.is_leap_year docstring
Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [X] PR title is "DOC: update the <your-function-or-method> docstring"
- [O] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [X] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [X] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```
################################################################################
################### Docstring (pandas.Series.dt.tz_convert)  ###################
################################################################################

Convert tz-aware DatetimeIndex from one time zone to another or remove
time zone information (using pytz/dateutil)

Parameters
----------
tz : string, pytz.timezone, dateutil.tz.tzfile or None
    Time zone for time. Corresponding timestamps would be converted to
    time zone of the TimeSeries.
    None will remove timezone holding UTC time.

Returns
-------
normalized : DatetimeIndex

Raises
------
TypeError
    If DatetimeIndex is tz-naive.

See Also
--------
tz_localize : Localize tz-naive DatetimeIndex to given time zone.

Examples
--------
>>> ts = pd.Series([pd.Timestamp("2018-01-01 00:00", tz="US/Eastern"),
...                 pd.Timestamp("2018-02-01 23:00", tz="US/Eastern")])
>>> ts.dt.tz_convert("Europe/Amsterdam")
0   2018-01-01 06:00:00+01:00
1   2018-02-02 05:00:00+01:00
dtype: datetime64[ns, Europe/Amsterdam]

>>> import pytz
>>> london = pytz.timezone('Europe/London')
>>> ts = pd.Series([pd.Timestamp("2018-01-01 00:00", tz=london)])
>>> ts.dt.tz_convert(tz=None)
0   2018-01-01
dtype: datetime64[ns]

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

Errors found:
	No summary found (a short summary in a single line should be present at the beginning of the docstring)
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'tz'}
```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

Comments:
- I don't know why is tz reported as unknown - it's declared and commented
- Summary is a bit longer that one row, I hope that is still ok.
On top of your summary, could you please add one more short sentence like "Converting time-zone for Datetime" below the """?

Could you also add:
args
            Additional arguments passed to the function
kwargs
            Additional keyword arguments passed to the function ```
Validator now reports:

################################################################################
################### Docstring (pandas.Series.dt.tz_convert)  ###################
################################################################################

Change time zone of Datetime series.

Convert tz-aware DatetimeIndex from one time zone to another or remove
time zone information. The time zone can be specified using a string,
pytz or dateutil.

Parameters
----------
tz : string, pytz.timezone, dateutil.tz.tzfile, None
    Time zone for time. Corresponding timestamps would be converted to
    time zone of the TimeSeries.
    None will remove timezone and return UTC timestamp.
args : iterable, optional
    Additional arguments passed to the function.
kwargs : dict, optional
    Additional keyword arguments passed to the function.

Returns
-------
normalized : DatetimeIndex

Raises
------
TypeError
    If DatetimeIndex is tz-naive.

See Also
--------
tz_localize : Localize tz-naive DatetimeIndex to given time zone.

Examples
--------
>>> ts = pd.Series([pd.Timestamp("2018-01-01 00:00", tz="US/Eastern"),
...                 pd.Timestamp("2018-02-01 23:00", tz="US/Eastern")])
>>> ts.dt.tz_convert("Europe/Amsterdam")
0   2018-01-01 06:00:00+01:00
1   2018-02-02 05:00:00+01:00
dtype: datetime64[ns, Europe/Amsterdam]

>>> import pytz
>>> pacific = pytz.timezone('US/Pacific')
>>> ts = pd.Series([pd.Timestamp("2018-01-01 01:00", tz=pacific)])
>>> ts.dt.tz_convert(tz=None)
0   2018-01-01 09:00:00
dtype: datetime64[ns]

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

Errors found:
	Errors in parameters section
		Unknown parameters {'tz'}
``` This was merged in https://github.com/pandas-dev/pandas/pull/20150