Logistic Regression is simple to implement, supports binomial, multinomial, and ordinal classification, and is a key layer in NN, as it's often used as an actuator that sorts a probability into a discrete category. Very good for specialized problems, and easily trainable to sort unknown or nonsensical inputs into a noncategory.

Linear Regression is great for projections, and can even be fit to time series data using lagging.