DOC: Rolling with method="table" to apply func to all columns and not just individual columns
#### Is your feature request related to a problem?

With a `group.apply` the applied function acts on all columns available to provide a result. With `rolling.apply` it applies the function to each column individually - it would be great if you could apply it to all the columns as well.

e.g. the following is possible with `groupby.apply`

```
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df['index'] = df['index'] % 3
df.groupby('index').apply(lambda df: df['A'] + df['B'])
```

However, you can't do the same equivalent with `rolling.apply`:

```
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df.rolling(3, on='index').apply(lambda df: (df['A'] * df['B']).sum())
```

Gives the error: `KeyError: 'A'`

This is because the `apply` function is applied to each column separately. The series is passed to the function not the dataframe. This feels inconsistent and annoying if you want to apply a function to the grouped rolling data. It would be great if there were a way to do it 😃 


