Transfer learning is a powerful technique in Machine Learning where a pre-trained model, which has been trained on a large dataset for a specific task, is utilized as a starting point for a different but related task. By leveraging the learned representations from the initial task, the model can often achieve better performance and require less data for training. For example, a model trained on a large dataset for image classification can be fine-tuned for object detection or even for tasks in completely different domains like natural language processing.

Transfer learning is when a pre-trained model is used as a foundation for another model. This can be useful because the pre-trained model has already learned general features from a large dataset, which can help the new model to learn better and more efficiently, especially when the new dataset is limited. It is like starting with a head start, which saves time and resources in the training process. An example would be using a pre-trained model for sentiment analysis to build a new model for classifying movie reviews.