The main purpose of an optimization algorithm in machine learning is to improve the accuracy and performance of a model by finding the best parameters that minimize or maximize a specific objective function. These algorithms adjust model parameters based on data to refine the predictions made by the model. Common optimization techniques include gradient descent and genetic algorithms. By efficiently updating model parameters, optimization algorithms help achieve better results in tasks like classification, regression, or clustering in machine learning applications.