to kill the Minotaur, but the true danger is not only the monster itself. It is the risk of losing all sense of direction inside a maze so complex that finding the way back becomes as difficult as the battle. This is where Ariadne’s thread matters: it represents the discipline needed to move through complexity without getting lost. In machine learning (ML), the visible challenge may be to beat a metric (the Minotaur), but the real risk lies in the maze built around it: hidden assumptions, fragile defaults, data leakage, misleading evaluation choices, unnecessary complexity, and preprocessing decisions that can make a model look stronger than it really is. This article follows that thread, focusing on how apparently strong results can emerge for the wrong reasons, and why maintaining methodological orientation is often more important than choosing the most sophisticated path. Reality check In ML, it is not unusual for an early prototype to produce striking metrics.…