Using a simple average of past performance to predict future performance is also a technique that is often disturbingly effective vs. standard practice. I suppose technically that is a linear model, but really deserves its own class.
Specifically, the Makridakis M forecasting competitions (https://en.wikipedia.org/wiki/Makridakis_Competitions) have shown for a long time that beating the baselines is shockingly difficult.
In fact, classic machine learning only really started to convincingly win with the second-to-last, M5: https://www.sciencedirect.com/science/article/pii/S016920702... ; and neural methods only just sort of began working with the latest one, M6: https://www.sciencedirect.com/science/article/pii/S016920702... . (Possibly with M7 we'll see scaled-up meta-learning Transformers finally start beating the Bayesian or decision-tree forecasters. But I don't know if or when a M7 might be held.)
They do not have sufficient explicit risk or variance management. Makes them highly fragile. There are more robust versions of the estimators... Still have a problem.
Remember 2008? That market ran on these easy models.