Add dataset conversion between sklearn and pandas dataframe
#### Problem description

There are several convenient datasets available in `sklearn`.  They consistently return the [`Bunch`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer) `sklearn` object. Therefore, it would not be too difficult to create a function to convert from `sklearn` into a nice, clean `pandas` dataframe.

#### Proposal

I am happy to take on the task of creating this conversion, but I'd like to get some input so that I create what users would actually want, and ideally my pull request would be accepted.

Below is a toy example of how I would expect the API to work, where `read_sklearn` is the name of the new method. (I'm happy to call it whatever, that just seemed like a good guess.)

```
>>> from pandas import read_sklearn
>>> from sklearn.datasets import load_iris as sk_load_iris
>>> sk_iris = sk_load_iris()
>>> pd_iris = read_sklearn(sk_iris)
>>> pd_iris.head()

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
0                5.1               3.5                1.4               0.2     setosa
1                4.9               3.0                1.4               0.2     setosa
2                4.7               3.2                1.3               0.2     setosa
3                4.6               3.1                1.5               0.2     setosa
4                5.0               3.6                1.4               0.2     setosa

>>> pd_iris.dtypes
sepal length (cm)     float64
sepal width (cm)      float64
petal length (cm)     float64
petal width (cm)      float64
target                category
dtype: object

>>> type(pd_iris)
<class 'pandas.core.frame.DataFrame'>
```

Would this be desirable?  Is this API I propose reasonable?  What should I consider in the API?  (E.g., in the example above, I have taken the fact that the `sklearn` dataset object has both a `target` and `target_names`, which means that I could infer that this should be a categorical. This can obviously make things a bit more fragile. Should this inference be skipped?

