The MinMaxScaler is a data preprocessing technique used in machine learning to normalize features. It transforms the data by scaling each feature to a specified range, typically between 0 and 1. This helps ensure that all features contribute equally to the model's performance, preventing any single feature from dominating due to its larger scale. By applying the MinMaxScaler, the minimum value of each feature becomes 0, and the maximum value becomes 1. This scaling is particularly useful for algorithms that rely on distance calculations, such as k-nearest neighbors and support vector machines, as it improves convergence and accuracy.