Unit Variance Scaling is a data preprocessing technique used to standardize features by ensuring each has a variance of one, which helps in improving the performance of machine learning algorithms. This technique is particularly useful when features are on different scales, as it ensures that each feature contributes equally to the distance calculations in algorithms sensitive to feature magnitude, such as k-nearest neighbors or support vector machines.