As mentioned by @Mark, Ridge Regression reduces the model complexity by coefficient shrinkage, i.e. here the magnitude of the coefficients decreases, the values reaches to zero but not absolute zero.
In case of Lasso Regression, our coefficients reduce to absolute zero. Therefore, lasso selects only some feature while reduces the coefficients of others to zero. This property is known as feature selection and which is absent in case of ridge.
Lasso Regression is generally used when we have more number of features, because it automatically does feature selection. Whereas, if we have less number of features or we don’t want to loose any feature we can use Ridge Regression.