Feature selection is a critical step in preparing data for machine learning, involving the selection of relevant features that improve model performance. It is distinct from feature extraction and dimensionality reduction, focusing solely on choosing the best subset of existing features for training. The importance of feature selection arises from avoiding irrelevant and redundant features, reducing the curse of dimensionality, minimizing training and deployment times, enhancing model interpretability, adhering to the principle of Occam's Razor, and ensuring data-model compatibility. Various methods exist for feature selection, including unsupervised, supervised (wrapper, filter, and embedded), and Boruta, a robust algorithm that uses random forests to select features without human input. In practice, combining multiple feature selection methods into a voting selector can enhance accuracy by leveraging the strengths of each approach. The article also highlights how large tech companies like Google and Facebook utilize feature selection to optimize their machine learning models and manage resources efficiently.