A Min-Max Scaler is a data normalization technique used in machine learning to transform features to a specific range, typically between 0 and 1. This is achieved by subtracting the minimum value of the feature and then dividing by the range (the difference between the maximum and minimum values). This scaling helps improve the performance of algorithms that are sensitive to the scale of data, such as k-nearest neighbors and neural networks. By using a Min-Max Scaler, you ensure that all features contribute equally to the distance calculations in algorithms. This is particularly important when features have different units or scales, as it prevents features with larger ranges from dominating the learning process. Overall, the Min-Max Scaler is a simple yet effective way to prepare data for analysis.