Min-Max Scaling is a normalization technique used to transform features to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the range of the feature. This process ensures that all features contribute equally to the distance calculations in algorithms like k-nearest neighbors and helps improve the convergence speed of gradient-based optimizers in models like neural networks.