Feedback plays a crucial role in the machine learning training process by guiding the model's learning and improving its performance. During training, the model makes predictions and is then evaluated against expected outcomes; the difference is known as the loss. This feedback is used to adjust the model parameters through techniques like backpropagation. Continuous feedback allows the model to iterate and enhance its accuracy over time, refining its ability to make decisions based on new data.