Normalization is a crucial data preprocessing technique used in machine learning and data science to adjust the scale of data features. It involves transforming the data to fit within a specific range, typically between 0 and 1, which helps to minimize discrepancies in feature scales. This process is essential when dealing with datasets that contain features measured in different units or with varying ranges, as it enhances the performance of algorithms, particularly those based on distance computations. By ensuring that no single feature dominates the model due to its scale, normalization improves the interpretability and effectiveness of machine learning models.