Pre-training and fine-tuning is a two-step process in machine learning where a model is first trained on a large dataset to learn general features, and then fine-tuned on a smaller, task-specific dataset to optimize its performance for a particular application. This approach leverages transfer learning to improve efficiency and effectiveness, especially in scenarios with limited labeled data.