I work with ML regularly, and there is always something new to learn!

Another commenter mentioned L1 regularization, which is useful for linear regression. You wouldn't use it for all classes of problems. L1 regularization has to do with minimizing error of absolute values, instead of squared errors or similar.

I skimmed this article and thinks it's accessible: https://www.kdnuggets.com/2021/06/feature-selection-overview...

PCA is a form of dimensionality reduction, but it doesn't select features for you.