Feature selection is a process used in machine learning and statistics to identify and select the most relevant variables or features from a dataset. By focusing on the most important features, models can become more efficient, improve accuracy, and reduce the risk of overfitting, which occurs when a model learns noise instead of the underlying pattern. There are various methods for feature selection, including filter methods, wrapper methods, and embedded methods. Filter methods evaluate features based on their statistical properties, while wrapper methods assess subsets of features based on model performance. Embedded methods incorporate feature selection as part of the model training process.