Fine-tuning involves starting with a model that has already been through initial training, known as a pre-trained model. This model has learned a lot about general data patterns but may not be directly applicable to a specific task. By feeding it task-specific data, the model can be adjusted—hence 'fine-tuned'—to perform better on desired outcomes, much like tailoring a suit for a particular individual.