Stability selection is a method for variable selection and structure estimation in high-dimensional data. It combines subsampling with selection algorithms to provide finite sample control for false discoveries and improve the reliability of variable selection. The method is general and applicable to various tasks such as regression, graphical modelling, and clustering. It addresses the challenge of choosing the right amount of regularization for consistent structure estimation. Stability selection has been shown to be effective for variable selection and Gaussian graphical modelling, using both real and simulated data. It provides a transparent principle for choosing regularization and improves variable selection and structure estimation for a range of selection methods. The method has been proven to be variable selection consistent even when the necessary conditions for consistency of the original Lasso method are violated. Stability selection is based on subsampling and randomization, and it has been shown to provide finite sample control for error rates and improved structure estimation. The method has been demonstrated for variable selection and Gaussian graphical modelling, and it has been shown to be effective in controlling false discoveries and improving variable selection. The method is applicable to a wide range of applications and has been shown to be effective in various settings.Stability selection is a method for variable selection and structure estimation in high-dimensional data. It combines subsampling with selection algorithms to provide finite sample control for false discoveries and improve the reliability of variable selection. The method is general and applicable to various tasks such as regression, graphical modelling, and clustering. It addresses the challenge of choosing the right amount of regularization for consistent structure estimation. Stability selection has been shown to be effective for variable selection and Gaussian graphical modelling, using both real and simulated data. It provides a transparent principle for choosing regularization and improves variable selection and structure estimation for a range of selection methods. The method has been proven to be variable selection consistent even when the necessary conditions for consistency of the original Lasso method are violated. Stability selection is based on subsampling and randomization, and it has been shown to provide finite sample control for error rates and improved structure estimation. The method has been demonstrated for variable selection and Gaussian graphical modelling, and it has been shown to be effective in controlling false discoveries and improving variable selection. The method is applicable to a wide range of applications and has been shown to be effective in various settings.