Data shuffling is a technique used in the field of data science and machine learning to enhance the randomness and diversity of data samples during the training process. By shuffling the data, researchers can prevent the model from learning patterns based on the order in which the data is presented, thus improving the model's generalization and performance on unseen data. This technique is particularly important when working with sequential data or time series data, where the order of samples may introduce biases in the model. Data shuffling is a common practice in various machine learning algorithms such as neural networks, decision trees, and support vector machines.