Add dimensionality reduction to AutoMLSearch

The exploration of the performance of one of our perf test datasets in #2628 raised the notice that the dataset has too many dimensions when compared to the number of data points, and performance significantly suffers because of it. We have dimensionality reduction components (both [PCA](https://github.com/alteryx/evalml/pull/1270) and [LDA](https://github.com/alteryx/evalml/pull/1331)), but right now we have no convenient way to add these to `AutoMLSearch`. I see two different ways we could make this easier when given high-dimensional datasets:
- Add a `HighDimensionalityDataCheck` that checks if the ratio of data points to number of features is too high, and an easy flag to add to search that would automatically include dimensionality reduction components in pipelines.
- With the addition of the new Default Algorithm and its new "long mode", add testing dimensionality reduction components in pipelines to said long mode to maintain model understanding in fast mode but potentially improve long mode performance.