df.sort_index() leaves us with df.index.is_lexsorted()==False
#### Code Sample, a copy-pastable example if possible

```
d=pd.DataFrame(
    [
        [1,2,3],
        [1,2,4],
        [2,3,5],
        [1,None,5],
    ],
    columns=['i','j','k']
)
print d.dtypes
d = d.set_index(['i','j'])['k']
#d = d.sort_index(level=[0,1])
d = d.sort_index()
print d.loc[(2,3)]   # PerformanceWarning even though sorted
```
#### Problem description

This behavior his is very confusing. The [docs ask me to sort multiindexes](https://pandas.pydata.org/pandas-docs/stable/advanced.html) to avoid the performance warning,
  
If I do it, I still get the PerformanceWarning. If I print the dataframe, I see that data was indeed sorted.

This is resolved by explicitly specifying the levels to sort: `d.sort_index(level=[0,1])`.

As a user, I'm confused. Wasn't `d.sort_index()` the same thing as `d.sort_index(level=...all levels...)`? Inspecting my dataframe, it appeared to be the, case (the index was sorted, and nested into a hierarchy).

Shouldn't  `d.sort_index()` simply be the same as `d.sort_index(level=...all levels...)`

I'm surprised there's any behavioral difference to be?

It seems due to:
```
d = d.sort_index()
print d.index.is_lexsorted()  # False

d = d.sort_index(level=[0,1])
print d.index.is_lexsorted()  # True
```
and then, they are only different if the index contains nulls.

Why there's a difference doesn't seem to be documented.

#### Expected Output

Either: it should be that `d.sort_index()`  === `d.sort_index(level=...all levels...)`
or, [ dataframe.sort_index documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_index.html) could stand improvement and explain when the fully specified level parameter is different to not specifying level.

I'd prefer:  ```d.sort_index()  === d.sort_index(level=d.index.names)```, because:
-  this would allow functions function working on dataframes with vanilla indexes and calling sort_index(), also work optimally when provided dataframes with multindexes.
- its cumbersome and error-prone to need to remember to specify the level names, since this is only needed in the multi-index case, and then also only in the case where the index contains NULLs.


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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.2.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
