#Cross-Validation

The covert arbiter that shatters model vanity by fragmenting training data and sacrificing validation sets, relentlessly exposing both engineer overconfidence and overfitting. Proclaiming itself a statistical safeguard, it endlessly questions what, if anything, can truly be trusted.