Answer
Feature selection is the process of choosing the most useful input variables, called features, for a machine learning model while removing ones that are irrelevant or redundant.
Real-world example A hospital predicting patient risk may start with fifty medical measurements but use feature selection to narrow it down to the ten most useful ones, making the model faster and easier to explain to doctors.