The MinMax Scaler 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 is done by subtracting the minimum value of the feature and then dividing by the range (maximum - minimum). This ensures that all features contribute equally to the model's performance. Using the MinMax Scaler is particularly important when features have different units or scales, as it helps improve the convergence speed of algorithms like gradient descent. It is commonly applied in various machine learning tasks, including classification and regression, to enhance model accuracy and efficiency.