Feature scaling is a data preprocessing step used to normalize the range of independent variables or features of data, ensuring that each feature contributes equally to the distance calculations in algorithms like k-nearest neighbors and gradient descent. It helps improve the performance and convergence speed of machine learning models by preventing features with larger magnitudes from dominating the learning process.