Feature selection is the process of selecting a subset of relevant features or variables from a larger set of available features, in order to improve the performance of a machine learning model. This can be done to reduce overfitting, improve model accuracy, and increase model interpretability. Feature selection methods can be filter-based, wrapper-based, or embedded, and may involve techniques such as correlation analysis, recursive feature elimination, and principal component analysis. Overall, feature selection plays a crucial role in building effective and efficient machine learning models.