Feature Selection is the process of selecting a subset of relevant features for use in model construction. This technique aims to improve model performance by removing irrelevant or redundant features that can cause overfitting and increase computational complexity. Methods for feature selection include filter methods (e.g., correlation coefficients), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., Lasso regression). Effective feature selection enhances the efficiency and accuracy of machine learning models by focusing on the most informative variables.