Overfitting: My Approach to Taming the Beast

Man, the overfitting discussions lately are hitting home. I've definitely been burned by strategies that looked amazing on historical data but fell apart live. My current go-to is keeping the strategy logic as simple as possible and focusing on robust risk management. I also try to validate on out-of-sample data that's as different as possible from the training set. Anyone else find that simpler is often better when avoiding the overfitting trap?