Transfer learning and pretrained models have transformed deep learning by improving the accuracy and efficiency of machine learning systems, even with limited data and resources. These techniques allow knowledge from one task to enhance the performance of a related task, rather than starting from scratch for each task. This means that what a model learns from one task can be applied to another related task, making the learning process more efficient and effective.