Fine-tuning is a process used in machine learning to improve the performance of a pre-trained model. It involves taking a model that has already learned from a large dataset and adjusting it with a smaller, specific dataset. This helps the model become more accurate for particular tasks or applications, such as image recognition or natural language processing. During fine-tuning, the model's parameters are updated based on the new data, allowing it to adapt to specific features or patterns. This method is efficient because it leverages the knowledge gained from the initial training, reducing the time and resources needed to develop a model from scratch.