Ablation studies systematically isolate the impact of individual model components by removing or modifying one element at a time and measuring the effect, providing scientific rigor for understanding what actually drives model performance. Purpose: distinguish necessary components from optional ones; understand contribution of each design choice; validate that claimed innovations actually help. Methodology: establish baseline (full model performance), remove/modify one component, measure performance change, and repeat for each component. Single-variable: change only one thing at a time; multiple simultaneous changes confound conclusions. Common ablations in ML: remove attention heads, replace activation functions, reduce model depth/width, remove data augmentation, change loss components, and disable regularization. Reporting: clearly document baseline, exactly what was changed, and quantitative performance impact (with error bars if possible). Controls: ensure fair comparison (same training budget, hyperparameter tuning for ablated versions). What ablations reveal: some "essential" components may not help; interactions between components; sensitivity to design choices. Publication standard: reviewers expect ablation studies justifying architectural choices. Beyond removal: can also study replacement (substitute component A for B) or addition (does adding X help?). Well-designed ablation studies separate causation from correlation in model design.