Data splitting is a fundamental technique in data science and machine learning, where a dataset is divided into distinct subsets for training, validation, and testing purposes. This process ensures that models are trained on one portion of the data while being evaluated on another, preventing overfitting and providing a more accurate assessment of model performance. Common splitting ratios include the 80-20 rule, where 80% of the data is used for training and 20% for testing, or the 70-20-10 rule, which includes a validation set. Proper data splitting is crucial for developing robust and reliable predictive models.