Why is PCA sometimes used as Pre$-$processing step before regression?

To make computation faster by reducing dimensionality of data

To reduce overfitting by removing poorly predictive dimensions

To expose information missing from the input data

For inference and scientific discovery, we prefer features that not axis aligned