Introduction
An estimator is a recipe that turns data into a guess of a parameter, like "take the sample mean". Since data are random, the guess is random too. Good estimators are right on average (low bias), don't jump around much between datasets (low variance), and get better with more data (consistency). The same trade-off between bias and variance governs model selection in ML.