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You are building a classification model to predict customer churn for a telecom company. The dataset includes features such as call duration, monthly charges, customer service calls, and contract type. After training the initial model, you notice it is overly complex and exhibits high variance, resulting in poor predictive accuracy on unseen data. Which regularization technique should you apply to improve the model?