Feature selection is a methodological approach within data science and machine learning focused on identifying and choosing a subset of relevant features from a larger set of initial variables. This process aims to enhance model performance, reduce computational complexity, improve interpretability, and mitigate the risk of overfitting by discarding irrelevant or redundant features.