Flattening in data preprocessing is crucial for transitioning from feature extraction to classification in machine learning models. Key practices include visualizing output dimensions, normalizing input data, and augmenting datasets to enhance generalization. Consider image size and experiment with activation functions in dense layers to optimize performance.