Model debugging: Add ability to compute and store graphs/stats on each CV fold during automl search

**Goal**
If users intend to compute graphs and stats on any of the models trained during each CV fold, we should design an API which allows them to do so.

I think we should make this general enough to support running any function on each CV fold and storing the results on the automl object. The results would be most naturally stored in the automl `results` dict, but we should build a public API to access the results.

**Proposal**
Add a `cv_callbacks` list as an init arg to `AutoMLSearch`, default None/empty. If provided, each callback will be run during the evaluation of each CV fold. The callback will be passed the training split, the validation split and the fitted model. The results from the callback will end up in `results` in each CV splits' dict, as a list stored under the name `cv_callback_results`

**Next Steps**
Before we build this, we should ask ourselves why this feature is necessary. Given our current data splitting plan, we want the ability to generate graphs and stats on the 1st CV fold, to correspond with the "validation" score which will be computed on the same data (see #1049). However, if we only need to compute graphs/stats on the first validation fold, or if we decide to create a separate split for model selection, the requirements here will change. So:
* Discuss the above point ( @gsheni @jeremyliweishih )
* If we should still proceed, write a design doc.

**Background**
We used to have this ability, but we removed it because we were mixing the `Objective` abstraction with the graph/stats methods. That's why I advocate for a design which is agnostic to the type of data being computed.