Normalization is a crucial data preprocessing technique used in data science and machine learning to rescale features into a standard range, typically between 0 and 1. This process is particularly beneficial when dealing with datasets that contain features with varying scales, as it helps to minimize the impact of these differences on model performance. By transforming the data, normalization ensures that algorithms, especially those based on distance calculations, can operate more effectively. It is essential for improving the accuracy and efficiency of machine learning models, making it a fundamental step in data preparation.