To try out regularization techniques, students should pay attention to these methods:

1. L1 Regularization (Lasso): This method adds a penalty based on the absolute value of the coefficients. This can make the solutions simpler. Studies show that Lasso can make models less complex and easier to understand, all while keeping them accurate.

2. L2 Regularization (Ridge): This technique adds a penalty based on the square of the coefficients. Ridge helps reduce the risk of overfitting, which means it helps models not get too tailored to the training data. Research shows that using L2 can lower model variance by about 50%.

3. Elastic Net: This method mixes L1 and L2 penalties, providing more flexibility. One study showed that models using Elastic Net did better than regular regression, improving predictive accuracy by 10%.

4. Hyperparameter Tuning: Use methods like cross-validation to find the best regularization amount, known as ($\lambda$). For example, a common range for $\lambda$ might be from $10^{-4}$ to $10^1$.

By trying out these techniques step by step, students can see how they affect bias and variance, helping them improve their models.