API: inconsistencies between grouped and grouped[['col']]  in groupby
One a DataFrameGroupBy object, you can already select some of the columns you want to apply your functions on, but apart from that, I would not expect it to influence the output structure:

```
In [56]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar'],
    ...:                    'B' : np.random.randn(4),
    ...:                    'C' : np.random.randn(4)})
    ...:                    

In [57]: 

In [57]: gr = df.groupby('A')

In [58]: gr.agg({'B': {'r': np.sum}, 'C': {'r2': np.sum}})
/home/joris/scipy/pandas/pandas/core/groupby/groupby.py:4650: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version
  return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
Out[58]: 
            C         B
           r2         r
A                      
bar -0.371496  1.230346
foo  0.091779  2.487224

In [59]: gr[['C', 'B']].agg({'B': {'r': np.sum}, 'C': {'r2': np.sum}})
/home/joris/scipy/pandas/pandas/core/groupby/groupby.py:4650: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version
  return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
Out[59]: 
           r2         r
A                      
bar -0.371496  1.230346
foo  0.091779  2.487224
```

So in the above, doing `gr[['C', 'B']]` causes the final result not to have the MultiIndex, which IMO is very surprising.

The above example if for a deprecated case, so not sure how important it is (didn't directly see a similar case for a non-deprecated call), but when we clean-up the deprecations, we should certainly check this.

cc @jreback @WillAyd 