Intuitively, if λ is set to a small value, optimization will attempt to learn a w that effectively minimizes loss on the training data, but there is a risk of overfitting reducing generalization capability.
if λ is set to a larger value, optimization will be less aggressive in minimizing loss on the training data, reducing over-fitting, but also possibly failing to capture useful information that could be used to improve accuracy.