Pre-training is the process of training a model on a large dataset to initialize its parameters before fine-tuning it on a smaller, task-specific dataset. This phase helps the model learn general features from the vast data, making it better suited for various tasks during fine-tuning. It increases efficiency and accuracy, as seen in models like BERT or GPT, where pre-training enables faster convergence and lower computing requirements during task-specific training.