Feature selection is the process of selecting a subset of relevant features for training machine learning models. It reduces the complexity of models, improves performance, and helps prevent overfitting by eliminating irrelevant or redundant features. Popular methods include recursive feature elimination, mutual information, and feature importance from decision trees.