BUG:nlargest/nsmallest does not act according to documentation with nan values
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample, a copy-pastable example

```python
import pandas as pd
import numpy as np

x = pd.Series([np.nan, np.nan, 1, 2, 3])

print(x.nlargest(n=4))
# 4    3.0
# 3    2.0
# 2    1.0
# dtype: float64

print(x.sort_values(ascending=False).head(4))
# 4    3.0
# 3    2.0
# 2    1.0
# 0    NaN
# dtype: float64
```

#### Problem description

The [docs](https://pandas.pydata.org/docs/reference/api/pandas.Series.nlargest.html) state:

> Faster than `.sort_values(ascending=False).head(n)` for small `n` relative to the size of the Series object.

which sort of suggests they are the same when they are not.

#### Expected Output

I think the sort/head approach gives the correct answer here, so we should have:

``` python
print(x.nlargest(n=4))
# 4    3.0
# 3    2.0
# 2    1.0
# 0    NaN
# dtype: float64
```

or the difference should be documented.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python           : 3.9.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-80-generic
Version          : #90~18.04.1-Ubuntu SMP Tue Jul 13 19:40:02 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.3.1
numpy            : 1.21.1
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.3
setuptools       : 57.4.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
