Normalization is a crucial data preprocessing technique in data science and machine learning, aimed at rescaling features to a common range, typically between 0 and 1. This process is particularly beneficial when dealing with datasets that contain features measured on different scales, as it helps to mitigate the impact of varying magnitudes 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 the data preparation process.