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ABSTRACT Accounting for uncertainty in model selection is crucial for statistical inference and data‐driven decision‐making, particularly with high‐dimensional data. While multiple studies have focused on constructing model confidence sets, a practical and informative visualization tool to assist in decision‐making under such uncertainty has been lacking. This paper introduces an intuitive visualization tool, the graph of ranking from solution paths (GRASP), designed to provide instant insights into model selection uncertainty. Additionally, GRASP accounts for the uncertainty of variable importance, enabling decision‐makers to assess each variable under uncertainty. Based on an innovative selection procedure that utilizes the entire solution path, a feature importance score and bootstrap techniques, GRASP effectively visualizes the uncertainty of model selection, as demonstrated by our numerical examples. Furthermore, we propose a novel measure of uncertainty based on GRASP, providing a single‐number summary of selection uncertainty. This measure incorporates the concept of the flat norm, traditionally used in geometry and physics. Our simulation studies and numerical examples confirm that this measure accurately and robustly quantifies uncertainty.