Or you have the classic case where the model trains on the wrong things (e.g. the classic husky/wolf and snow case). One example I heard was in heart attack risk prediction from x-rays, the model was focusing on the present of stents in the x-rays. Which is a trivial prediction because the doctor already knows the patient has a stent, so the model is not adding anything of value.