Feature selection identifies and keeps only the most relevant input variables while discarding redundant or irrelevant ones. Fewer features mean faster training, lower overfitting risk, and easier interpretation. Techniques include correlation analysis, mutual information, recursive elimination, and L1 regularization. A credit scoring model might start with 200 customer attributes and find only 30 drive predictions.