Feedback loops enhance machine learning models by providing continuous data to improve algorithms iteratively. They enable models to learn from their predictions and refine their outputs based on feedback received from real-world applications. This process involves comparing predicted outcomes with actual results; discrepancies inform adjustments to model parameters. As a result, the model becomes more accurate over time, adapting to changes in data patterns. This iterative refinement is crucial in various applications, from natural language processing to predictive analytics, ensuring that models evolve and remain effective.