Min-Max Scaling is a technique used to normalize data by transforming features to a common scale, 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 method is particularly useful in machine learning algorithms that are sensitive to the scale of data, such as k-nearest neighbors and neural networks. By applying Min-Max Scaling, you ensure that each feature contributes equally to the distance calculations and model training. This helps improve the performance and convergence speed of algorithms, making it easier to interpret the results. It is important to note that Min-Max Scaling can be affected by outliers, as they can distort the minimum and maximum values.