Mean normalization is a data preprocessing technique used to scale features to have a mean of zero, enhancing the performance and convergence speed of machine learning algorithms. It is particularly useful in algorithms sensitive to feature scaling, such as gradient descent and k-means clustering, by ensuring that all features contribute equally to the distance calculations.