Naive Bayes is a simple yet effective classification algorithm based on Bayes' theorem. It assumes that the features used for classification are independent of each other, which is often not the case in real-world data. Despite this assumption, it performs surprisingly well in various applications, such as text classification and spam detection. The algorithm calculates the probability of each class given the input features and selects the class with the highest probability. Its efficiency and ease of implementation make Naive Bayes a popular choice for many machine learning tasks, especially when dealing with large datasets.