Row scaling is a data preprocessing technique that involves adjusting the values of each row in a matrix or dataset to ensure they have a consistent scale, often by normalizing or standardizing them. This process is crucial for improving the performance of machine learning algorithms, as it ensures that each feature contributes equally to the distance calculations in models like k-nearest neighbors or clustering algorithms.