The Standard Scaler is a data preprocessing technique used in machine learning to standardize features by removing the mean and scaling to unit variance. This process transforms the data so that it has a mean of zero and a standard deviation of one, making it easier for algorithms to learn patterns effectively. Standardization is particularly important for algorithms that rely on distance calculations, such as k-Nearest Neighbors and Support Vector Machines. By ensuring that all features contribute equally to the distance metrics, the Standard Scaler helps improve the performance and convergence speed of these models.