Naive Bayes is a probabilistic classifier based on Bayes' Theorem. It assumes that features are independent of each other, which is often called the "naive" assumption. Despite its simplicity, it works well for text classification problems, such as spam detection or sentiment analysis. 

Random Forest is a learning method that combines multiple decision trees to improve model accuracy and prevent overfitting. Each tree is built on a random subset of data and features, and the final prediction is made by averaging (for regression) or taking a majority vote (for classification) across all trees. It is widely used for both classification and regression tasks. 

Support Vector Machine is a supervised learning algorithm that aims to find the optimal boundary (hyperplane) that separates data points of different classes. SVM is particularly effective for high-dimensional data and is often used in applications like image recognition and text categorization. 

K-Nearest Neighbors is a simple and intuitive algorithm that classifies data points based on the majority class of their "k" nearest neighbors. It doesn't require a training phase, as it simply stores the training data and makes predictions by comparing the new data point to the stored ones. It is commonly used for classification tasks like recommendation systems and pattern recognition.