Normalization is a crucial data preprocessing technique used in data science and machine learning. It involves rescaling the features of a dataset to a standard range, typically between 0 and 1. This process helps to minimize the impact of varying scales and units among different features, ensuring that no single feature dominates the analysis. By transforming the data, normalization enhances the performance of machine learning algorithms, particularly those that rely on distance calculations, such as k-nearest neighbors and neural networks. Ultimately, normalization improves the interpretability and effectiveness of models by providing a more uniform dataset for analysis.