Coefficient shrinkage is a technique used in statistical modeling to prevent overfitting by penalizing large coefficients, effectively reducing model complexity. It is commonly implemented through regularization methods like Lasso and Ridge regression, which balance the trade-off between bias and variance in predictive models.