Dataset splitting is a crucial step in machine learning that involves dividing the data into subsets to train, validate, and test a model, ensuring its performance and generalization capabilities. Properly splitting datasets helps prevent overfitting and provides a reliable estimate of a model's predictive performance on unseen data.