Standardizing or normalizing features in Spark ML data preprocessing is essential to ensure that all features are on a similar scale. This is crucial because many machine learning algorithms, such as those based on distance metrics (e.g., k-Nearest Neighbors) or optimization algorithms (e.g., Gradient Descent), are sensitive to the scale of the features. If features have different scales, some features with larger magnitudes may dominate the learning process, leading to biased model training. Standardizing or normalizing features brings them to a comparable scale, preventing any single feature from having a disproportionate impact on the model, and allowing the algorithm to learn more effectively across all features.