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 with varying scales, as it helps to mitigate issues that arise in algorithms sensitive to the magnitude of input values. By transforming the data, normalization enhances the performance of machine learning models, ensuring that each feature contributes equally to the analysis. Understanding when and how to apply normalization is essential for effective data handling and model training.